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Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.

Papadopoulou MP, Nikolos Ioannis, Karatzas Giorgos

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URIhttp://purl.tuc.gr/dl/dias/F94A8E13-059C-4A6C-9065-6F430B8C16B7-
Identifierhttps://doi.org/10.2166/wst.2010.442.-
Languageen-
Extent11en
TitleComputational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.en
CreatorPapadopoulou MPen
CreatorNikolos Ioannisen
CreatorΝικολος Ιωαννηςel
CreatorKaratzas Giorgosen
CreatorΚαρατζας Γιωργοςel
DescriptionΔημοσίευση σε επιστημονικό περιοδικό el
Content SummaryArtificial Neural Networks (ANNs) comprise a powerful tool to approximate the complicated behavior and response of physical systems allowing considerable reduction in computation time during time-consuming optimization runs. In this work, a Radial Basis Function Artificial Neural Network (RBFN) is combined with a Differential Evolution (DE) algorithm to solve a water resources management problem, using an optimization procedure. The objective of the optimization scheme is to cover the daily water demand on the coastal aquifer east of the city of Heraklion, Crete, without reducing the subsurface water quality due to seawater intrusion. The RBFN is utilized as an on-line surrogate model to approximate the behavior of the aquifer and to replace some of the costly evaluations of an accurate numerical simulation model which solves the subsurface water flow differential equations. The RBFN is used as a local approximation model in such a way as to maintain the robustness of the DE algorithm. The results of this procedure are compared to the corresponding results obtained by using the Simplex method and by using the DE procedure without the surrogate model. As it is demonstrated, the use of the surrogate model accelerates the convergence of the DE optimization procedure and additionally provides a better solution at the same number of exact evaluations, compared to the original DE algorithm.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-21-
Date of Publication2010-
SubjectArtificial Neural Networksen
Subject Radial Basis Function Artificial Neural Network en
SubjectCreteen
SubjectDE algorithmen
Bibliographic CitationM.P. Papadopoulou , I.K. Nikolos, and G.P. Karatzas, "Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.,"Water Science and Technology, vol. 62, no. 7,pp. 1479-1490, 2010. doi: 10.2166/wst.2010.442.en

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